深度学习与混合正规化极端学习机器用于多个月的干旱预测:热带地区的比较研究和趋势分析
Mohammed Majeed Hameed1,2, Siti Fatin Mohd Razali1,3, Wan Hanna Melini Wan Mohtar1,3
1Green Engineering and Net Zero Solution (GREENZ), Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), 43600 UKM, Bangi, Selangor, Malaysia.
Heliyon
|January 8, 2024
概括
一个新的混合模型结合了规范化的极端学习机器和Snake算法,提供了卓越的水文干旱预测. 这种先进的方法提高了长期预测的准确性和可靠性,有助于水资源管理战略.
科学领域:
- 水文学的水文学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 干旱对水资源,农业和经济构成重大风险.
- 准确的干旱预测和趋势评估对于有效的水资源管理至关重要.
- 现有的干旱建模技术在精度上存在局限性,需要先进的方法.
研究的目的:
- 评估混合模型 (规范化的极端学习机器和Snake算法) 与长短期记忆 (LSTM) 和用于水文干旱预测的经典模型的有效性.
- 预测水文干旱,提前一到六个月使用溪流数据.
- 引入一个新的参数来评估模型在捕获数据转折点中的性能.
主要方法:
- 利用了马来西亚两个站点58年流量数据的多变量标准化流量指数 (MSSI).
- 将混合RELM-SO模型与LSTM,梯度增强回归和K-最近模型进行比较.
- 采用创新的趋势分析来评估水文干旱模式.
主要成果:
- 混合RELM-SO模型在两个站都表现出卓越的准确性,优于LSTM和经典模型,其RMSE和MAE较低,NSE和WI较高.
- 混合模型显示,与LSTM相比,预测准确度显著提高 (19.32%21.52%).
- 趋势分析表明,10月,11月和12月干旱发生率较高.
结论:
- 混合RELM-SO模型为水文干旱预测提供了更准确,更可靠的方法,特别是在长期预测方面.
- 该研究强调了先进模型和新的评估指标对于干旱预测的重要性.
- 调查结果为干旱地区的水资源管理提供了宝贵的见解.
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